We perform all experiments using single NVIDIA A40 GPUs. Unless otherwise specified, all the pre-training and transfer learning are run for 200 epochs using SGD with a momentum of 0.9, a weight decay of 0.0001 and a dynamical learning rate ranging from 0.1 to 0.0001 controlled by a CosineAnnealing learning rate scheduler as described in [1]}. When using ImageNet pre-trained models, we rescale each input image to \(224 \times 224\)  for training, testing and evaluate \(\mathcal {NC}\)  .
